# Beyond One-Size-Fits-All Summarization: Customizing Summaries for Diverse Users

arXiv: https://arxiv.org/abs/2503.10675 · HTML: https://mlsanity.com/paper/2503.10675/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 45 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [A Neural Attention Model for Abstractive Sentence Summarization](https://mlsanity.com/paper/1509.00685/) (2015)
- [XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](https://mlsanity.com/paper/2106.13822/) (2021)
- [GSum: A General Framework for Guided Neural Abstractive Summarization](https://mlsanity.com/paper/2010.08014/) (2020)
- [MLSUM: The Multilingual Summarization Corpus](https://mlsanity.com/paper/2004.14900/) (2020)
- [Neural Summarization by Extracting Sentences and Words](https://mlsanity.com/paper/1603.07252/) (2016)
- [SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF](https://mlsanity.com/paper/2310.05344/) (2023)
- [Discourse-Aware Neural Extractive Text Summarization](https://mlsanity.com/paper/1910.14142/) (2019)
- [CTRLsum: Towards Generic Controllable Text Summarization](https://mlsanity.com/paper/2012.04281/) (2020)
- [Controllable Sentence Simplification](https://mlsanity.com/paper/1910.02677/) (2019)
- [Controlling Output Length in Neural Encoder-Decoders](https://mlsanity.com/paper/1609.09552/) (2016)
- [Multi-Fact Correction in Abstractive Text Summarization](https://mlsanity.com/paper/2010.02443/) (2020)
- [Generating Summaries with Controllable Readability Levels](https://mlsanity.com/paper/2310.10623/) (2023)
- [Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings](https://mlsanity.com/paper/2110.07385/) (2021)
- [Readability Controllable Biomedical Document Summarization](https://mlsanity.com/paper/2210.04705/) (2022)
- [LR-Sum: Summarization for Less-Resourced Languages](https://mlsanity.com/paper/2212.09674/) (2022)
- [VBART: The Turkish LLM](https://mlsanity.com/paper/2403.01308/) (2024)

## Built on by (most cited first, 2 of 2)

- [TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation](https://mlsanity.com/paper/2604.07894/) (2026)
- [Ready to Speak: Aligning LLMs for TTS-Friendly Text Generation](https://mlsanity.com/paper/2609.01246/) (2026)

## Abstract (arXiv)

In recent years, automatic text summarization has witnessed significant advancement, particularly with the development of transformer-based models. However, the challenge of controlling the readability level of generated summaries remains an under-explored area, especially for languages with complex linguistic features like Turkish. This gap has the effect of impeding effective communication and also limits the accessibility of information. Controlling readability of textual data is an important element for creating summaries for different audiences with varying literacy and education levels, such as students ranging from primary school to graduate level, as well as individuals with diverse educational backgrounds. Summaries that align with the needs of specific reader groups can improve comprehension and engagement, ensuring that the intended message is effectively communicated. Furthermore, readability adjustment is essential to expand the usability of summarization models in educational and professional domains. Current summarization models often don't have the mechanisms to adjust the complexity of their outputs, resulting in summaries that may be too simplistic or overly complex for certain types of reader groups. Developing adaptive models that can tailor content to specific readability levels is therefore crucial. To address this problem, we create our own custom dataset and train a model with our custom architecture. Our method ensures that readability levels are effectively controlled while maintaining accuracy and coherence. We rigorously compare our model to a supervised fine-tuned baseline, demonstrating its superiority in generating readability-aware summaries.